We consider a similar setup to [1]}: given the start visual observation \(o_t\)  and a visual goal \(o_g\)  that indicates the desired outcome for a particular task. During training, we have access to the observation-action pairs \(\lbrace (o_{t:g}, a_{t:g})\rbrace \sim \pi _E\)  that were collected by an expert attempting to reach the goals. When testing, only the start visual observation \(o_t\)  and a visual goal \(o_g\)  are given. Our objective is to plan a sequence of actions \([\hat{a}_t, \dots , \hat{a}_{t+T-1}]\)  that can bring the underlying state of \(o_t\)  to that of \(o_g\) . \(T\)  is the horizon of planning, which means the number of task-level action steps the model is allowed to take.
Figure REF  shows a goal-oriented plannable example where the intermediate steps of performing a complex planning task are planned.
